Data Management Report Transformative Change Assessment Corpus - SOD

Author
Affiliation

Rainer M Krug

Published

February 20, 2024

Doi
Abstract

The literature search for the assessment corpus was conducted using search terms provided by the experts and refined in co-operation with the Knowldge and Data task force. The search was conducted using OpenAlex, scripted from R to use the API. Search terms for the following searches were defined: Transformative Change, Nature / Environment and additional search terms for individual chapters and sub-chapters To assess the quality of the corpus, sets of key-papers were selected by the experts to verify if these are in the corpus. These key-papers were selected per chapter / sub-chapter to ensure that the corpus is representative of each chapter.

Keywords

DMR, TCA, Assessment Corpus

DOI GitHub release GitHub commits since latest release License: CC BY 4.0

Working Title

IPBES_TCA_Corpus

Code repo

Github repository

Build No: 188

Introduction

The following terminology is used in this document:

  • Individual corpus: The corpus resulting from one search term, e.g. transformative or nature or ChX_Y
  • Assessment Corpus: The corpus resulting from the search terms transformative AND nature
  • Chapter corpus: The corpus resulting from transformative AND Nature AND ChX_Y

The following searches are conducted on Title and Abstrat only as the availability of fulltext drops in 2020. OpenAlex did “inherit” fro Microsoft Academic their initial corpus in 2021 which contained a lot of fulltext for searches. After that time, other sources had to be used which did not include fulltext for searches. To eliminate this bias, we linit the search for terms in abstract and title only.

Search Terms

Here are the search terms used in this document. They were provided by the authors, and some adaptations were done by the tsu to adopt them for OpenAlex.

Transformative Change

Show the code
cat(params$s_1_transformative_change)
(
    (
        (
            transformation
            OR transition
            OR transformative
            OR "transformative change"
        )
        OR (
            (
                shift
                OR change
            )
            AND (
                fundamental
                OR deep
                OR radical
            )
        )
    )
    AND (
        socio
        OR social
        OR politics
        OR political
        OR governance
        OR economic
        OR cultural
        OR system
        OR technological
        OR inner
        OR personal
        OR financial
        OR business
    )
) OR (
    (
        "transformative change"
        OR "deliberate transformation*"
        OR "transformative turn"
        OR transition
        OR "social-ecological change*"
        OR "deep change"
        OR "fundamental alteration"
        OR "profound change"
        OR "profound transformation"
        OR "radical transformation"
        OR "transformational change"
        OR "complete change"
        OR "complete transformation"
        OR "drastic change"
        OR "in-depth transformation"
        OR "progressive change"
        OR "radical alteration"
        OR "radical change"
        OR "revolutionary change"
        OR "significant modification"
        OR "total transformation"
        OR transition
        OR pathway
        OR power
        OR agency
        OR scale
        OR leverage
        OR context
        OR process
        OR regime
        OR shift
        OR views
        OR value
        OR structure
        OR institution
        OR deliberate
        OR structural
        OR fundamental
        OR system
        OR deep
        OR radical
        OR profound
        OR drastic
        OR widespread
        OR political
        OR economical
        OR structur
        OR complete
        OR progressive
        OR revolutionary
        OR substantial
        OR significant
    )
    AND (
        transformation
        OR alteration
        OR change
        OR turn
        OR action
        OR transition
        OR shift
    )
)

Nature

Show the code
#|

cat(params$s_1_nature_environment)
biodiversity OR marine OR terrestrial OR forest* OR woodland* OR grassland* OR savanna* OR shrubland* OR peatland OR ecosystem OR lake OR river OR sea OR ocean OR meadow OR heathland OR mires OR bog OR tundra OR biosphere OR desert OR mountain OR "natural resource" OR estuary OR fjord OR fauna OR flora OR soil OR "coastal waters" OR wetland OR freshwater OR marshland OR marches OR dryland OR seascape OR landscape OR coast OR "arable land" OR "agricultural land" OR "natural environment" OR "environmental resource" OR agroforest OR "agro-forest" OR plantation OR "protected areas" OR chaparral OR sustainable OR environment OR conservation OR ecosystem OR nature OR planet OR Earth OR biosphere OR ecological OR "socio-ecological" OR restoration OR wildlife OR landscape OR species OR bioeconomy OR "resource system" OR "coupled system" OR nature

Assessment Corpus

Show the code
#|

cat(params$s_1_f_tca_corpus())
((
    (
        (
            transformation
            OR transition
            OR transformative
            OR "transformative change"
        )
        OR (
            (
                shift
                OR change
            )
            AND (
                fundamental
                OR deep
                OR radical
            )
        )
    )
    AND (
        socio
        OR social
        OR politics
        OR political
        OR governance
        OR economic
        OR cultural
        OR system
        OR technological
        OR inner
        OR personal
        OR financial
        OR business
    )
) OR (
    (
        "transformative change"
        OR "deliberate transformation*"
        OR "transformative turn"
        OR transition
        OR "social-ecological change*"
        OR "deep change"
        OR "fundamental alteration"
        OR "profound change"
        OR "profound transformation"
        OR "radical transformation"
        OR "transformational change"
        OR "complete change"
        OR "complete transformation"
        OR "drastic change"
        OR "in-depth transformation"
        OR "progressive change"
        OR "radical alteration"
        OR "radical change"
        OR "revolutionary change"
        OR "significant modification"
        OR "total transformation"
        OR transition
        OR pathway
        OR power
        OR agency
        OR scale
        OR leverage
        OR context
        OR process
        OR regime
        OR shift
        OR views
        OR value
        OR structure
        OR institution
        OR deliberate
        OR structural
        OR fundamental
        OR system
        OR deep
        OR radical
        OR profound
        OR drastic
        OR widespread
        OR political
        OR economical
        OR structur
        OR complete
        OR progressive
        OR revolutionary
        OR substantial
        OR significant
    )
    AND (
        transformation
        OR alteration
        OR change
        OR turn
        OR action
        OR transition
        OR shift
    )
)) AND (biodiversity OR marine OR terrestrial OR forest* OR woodland* OR grassland* OR savanna* OR shrubland* OR peatland OR ecosystem OR lake OR river OR sea OR ocean OR meadow OR heathland OR mires OR bog OR tundra OR biosphere OR desert OR mountain OR "natural resource" OR estuary OR fjord OR fauna OR flora OR soil OR "coastal waters" OR wetland OR freshwater OR marshland OR marches OR dryland OR seascape OR landscape OR coast OR "arable land" OR "agricultural land" OR "natural environment" OR "environmental resource" OR agroforest OR "agro-forest" OR plantation OR "protected areas" OR chaparral OR sustainable OR environment OR conservation OR ecosystem OR nature OR planet OR Earth OR biosphere OR ecological OR "socio-ecological" OR restoration OR wildlife OR landscape OR species OR bioeconomy OR "resource system" OR "coupled system" OR nature)

Chapter 1

01

Show the code
#|

cat(params$s_1_ch1_01)
(
    root
    OR underlying
    OR indirect
) AND (
    driver
    OR cause
)

02

Show the code
#|

cat(params$s_1_ch1_02)
equity OR inequity OR just OR unjust OR inequality OR equality OR Fair OR unfair

03

Show the code
#|

cat(params$s_1_ch1_03)
scal* OR impact OR leapfrog OR transfer

04

Show the code
#|

cat(params$s_1_ch1_04)
inclusive OR participation OR participatory OR engagement OR democratic OR coproduct OR transdisc OR multiactor OR "multi-actor" OR integrat

05

Show the code
#|

cat(params$s_1_ch1_05)
evaluate OR reflex OR reflect OR monitor OR adapt OR learn

06

Show the code
#|

cat(params$s_1_ch1_06)
responsib OR accountable OR rights OR steward OR reciprocity OR interdependent OR interdependency OR (
    relation
    OR relational
    OR plural
    OR diverse
    OR "sustainability-aligned"
    OR (
        care
        AND (
            value
            OR ethic
        )
    )
)      

Chapter 2

Show the code
#|

cat(params$s_1_ch2)
vision OR future OR visionary OR scenarios OR imagination OR imagery OR creativity OR desire OR wish OR visioning OR process OR "participaory process" OR "deliberate process" OR polics OR target OR view OR value OR cosmovision OR cosmocentric OR dream OR fiction OR hope OR mission OR objective OR story OR worldview OR aspiration OR action OR plan OR strategy OR intention OR model OR solution OR innovation OR perspective OR platform OR collective action OR cooperation OR consultation OR coalition OR response OR movement OR effort OR initiative OR activity OR reaction OR performance OR operation OR effect OR task OR project OR influence  OR moment OR discourse OR motivation OR iteration OR roadmap OR agenda OR project OR programm OR government OR technique OR inspiration OR culture OR universe OR reality OR fantasy OR perception OR visualization OR approach OR image OR arquetype OR existence OR cosmology OR co-production OR knowledge OR dialogue OR transmission OR conceptual OR ceremony OR relationships OR respect OR reciprocity OR responsibilities OR solidarity OR harmony OR self-determination OR community OR spiritual OR languague OR territory OR opportunity OR sight OR foresight OR idea OR appearance

Chapter 3

01

Show the code
#|

cat(params$s_1_ch3_01)
Technolog* OR Science* OR "science-society" OR "science-technology" OR Solution

02

Show the code
#|

cat(params$s_1_ch3_02)
"co-create" OR "co-creation" OR solution OR knowledge OR system OR "t-lab" OR "technology laboratory" OR education OR "socio-technic*"

03

Show the code
#|

cat(params$s_1_ch3_03)
System OR pathways OR connect OR Agroecolog OR Institutional OR Institution OR Government

04

Show the code
#|

cat(params$s_1_ch3_04)
inner OR Personal OR Religion OR Love OR Loving OR Feelings OR Stewardship OR Care OR Beliefs OR Belief OR believe OR Awareness OR "Self-Awareness"

05

Show the code
#|

cat(params$s_1_ch3_05)
Worldviews OR Grassroot OR "Community-based" OR Indigenous OR Leadership OR "Critical Science" OR Econfeminism OR "Political Ecology" OR Power OR Agency OR Environment

06

Show the code
#|

cat(params$s_1_ch3_06)
Economic OR "Political Economy" OR institution OR govern OR economy OR governance OR government OR globalization OR states OR colonial OR colonialiasism OR labour OR organization*

Chapter 4

01

Show the code
#|

cat(params$s_1_ch4_01)
(
    challenge
    OR barrier
    OR obstacle
    OR hinder
    OR hindrance
    OR block
    OR prevent
    OR deter
    OR inertia
    OR "path dependence"
    OR "path dependency"
    OR stasis
    OR "lock-in"
    OR trap
    OR habits
    OR habitual
    OR "status quo"
    OR power
    OR "limiting factOR"
) AND (
    economic inequality
    OR "Wealth concentration"
    OR "Socioeconomic inequality"
    OR financialization
    OR "uneven development"
    OR Financialization
    OR "Structural adjustment"
    OR "Sovereign Debt"
    OR inequality
    OR "Policy effectiveness"
)

02

Show the code
#|

cat(params$s_1_ch4_02)
(
    challenge
    OR barrier
    OR obstacle
    OR hinder
    OR hindrance
    OR block
    OR prevent
    OR deter
    OR inertia
    OR "path dependence"
    OR "path dependency"
    OR stasis
    OR "lock-in"
    OR trap
    OR habits
    OR habitual
    OR status quo
    OR power
    OR "limiting factor"
) AND (
    "clean technolog*"
    OR "clean innovation*"
    OR "sustainable innovation"
    OR "sustainable technological innovation"
) AND (
    "limited access"
    OR "limited availability"
    OR "lack of access"
    OR "unavailability"
)

Chapter 5

Vision

Show the code
#|

cat(params$s_1_ch5_vision)

Case

Show the code
#|

cat(params$s_1_case)
"case study" OR case OR "study area" OR example OR evaluation OR concrete OR empirical OR practical OR initiative

Vision & Case

Topics

OpenAlex assigns topics to each work in a hirarchical manner:

Please see here for more information and here for a complete list of all topics and their corresponding subfields, fields and domains.

Get and calculate Data

In this section, the data is retrieved from OpenAlex and the calculations are done. It contains the code used. No results are shown here, so this section can be skipped.

Show the code
#|

fn <- file.path(".", "data", "search_term_hits.rds")
if (!file.exists(fn)) {
    s_t <- grep("s_1_", names(params), value = TRUE)
    search_term_hits <- parallel::mclapply(
        s_t,
        function(stn) {
            message("getting '", stn, "' ...")
            if (grepl("_f_", stn)) {
                search <- params[[stn]]()
            } else {
                search <- params[[stn]]
            }
            search <- compact(search)
            openalexR::oa_query(filter = list(title_and_abstract.search = search)) |>
                openalexR::oa_request(count_only = TRUE, verbose = TRUE) |>
                unlist()
        },
        mc.cores = params$mc.cores,
        mc.preschedule = FALSE
    ) |>
        do.call(what = cbind) |>
        t() |>
        as.data.frame() |>
        dplyr::mutate(page = NULL, per_page = NULL) |>
        dplyr::mutate(count = formatC(count, format = "f", big.mark = ",", digits = 0))

    rownames(search_term_hits) <- s_t |>
        gsub(pattern = "s_1_", replacement = "") |>
        gsub(pattern = "f_", replacement = "") |>
        gsub(pattern = "^ch", replacement = "Chapter ") |>
        gsub(pattern = "_", replacement = " ")

    saveRDS(search_term_hits, file = fn)
} else {
    search_term_hits <- readRDS(fn)
}
Show the code
#|

fn <- file.path(".", "data", "countries_tca_corpus.rds")
if (!file.exists(fn)) {
    countries_tca_corpus <- openalexR::oa_query(
        filter = list(
            title_and_abstract.search = compact(params$s_1_f_tca_corpus())
        ),
        group_by = "authorships.countries"
    ) |>
        openalexR::oa_request(count_only = FALSE, verbose = TRUE) |>
        sapply(FUN = unlist) |>
        t() |>
        as.data.frame() |>
        dplyr::rename(iso2c = key) |>
        mutate(count = as.numeric(count))

    saveRDS(countries_tca_corpus, file = fn)
} else {
    countries_tca_corpus <- readRDS(fn)
}

Downloading groups...
|===
Show the code
fn <- file.path(".", "data", paste0("sample_tca_corpus_", params$sample_size, ".rds"))
fn_df <- file.path(".", "data", paste0("sample_tca_corpus_", params$sample_size, ".rds"))
if (!file.exists(fn)) {
    message("Sampling 'transformative change' corpus (n = ", params$sample_size, ") - this can take some time ...")
    sample_tca_corpus <- openalexR::oa_query(
        filter = list(
            title_and_abstract.search = compact(
            paste0(
                "(", params$s_1_transformative_change, ") AND (", params$s_1_nature_environment, ")"
            )
            )
        ),
        options = list(
            sample = params$sample_size
        )
    ) |>
        openalexR::oa_request(count_only = FALSE, verbose = TRUE)

    sample_tca_corpus_df <- oa2df(sample_tca_corpus, entity = "works")

    saveRDS(sample_tca_corpus, file = fn)
    saveRDS(sample_tca_corpus_df, file = fn_df)
} else {
    sample_tca_corpus <- readRDS(fn)
    sample_tca_corpus_df <- readRDS(fn_df)
}
Show the code
fn <- file.path(".", "data", paste0("prim_topics_tca_corpus.rds"))
if (!file.exists(fn)) {
    # message("Sampling 'transformative change' corpus (n = ", params$sample_size, ") - this can take some time ...")

    prim_topics_tca_corpus <- openalexR::oa_fetch(
        title_and_abstract.search = compact(params$s_1_f_tca_corpus()),
        group_by = "primary_topic.id",
        verbose = FALSE
    )
    names(prim_topics_tca_corpus) <- c("topic_id", "topic_name", "count")
    prim_topics_tca_corpus <- prim_topics_tca_corpus[-2]
    prim_topics_tca_corpus$topic_id <- sub("https://openalex.org/T", "", prim_topics_tca_corpus$topic_id)
    prim_topics_tca_corpus <- merge(
        read.csv(file.path("inputs", "OpenAlex_topic_mapping_table - final_topic_field_subfield_table.csv")),
        prim_topics_tca_corpus,
        by = "topic_id"
    )

    prim_topics_tca_corpus <- prim_topics_tca_corpus[order(prim_topics_tca_corpus$count, decreasing = TRUE), ]

    saveRDS(prim_topics_tca_corpus, file = fn)
} else {
    prim_topics_tca_corpus <- readRDS(fn)
}
Show the code
#|

fn <- file.path(".", "data", paste0("nature_corpus_plus.rds"))
if (!file.exists(fn)) {
    nature_corpus_plus <- openalexR::oa_fetch(
        title_and_abstract.search = compact(params$s_1_nature_environment),
        concepts.id = paste(
            "C86803240",
            "C18903297",
            "C205649164",
            "C59822182",
            "C127313418",
            "C151730666",
            "C90856448",
            "C159390177",
            "C78458016",
            "C58640448",
            "C185933670",
            "C62649853",
            "C114793014",
            "C97137747",
            "C132651083",
            "C110872660",
            "C24518262",
            "C130217890",
            "C43827410",
            sep = "|"
        ),
        sustainable_development_goals.id = paste(
            "https://metadata.un.org/sdg/14",
            "https://metadata.un.org/sdg/15",
            "https://metadata.un.org/sdg/13",
            sep = "|"
        ),
        options = list(
            select = "id, authorships"
        ),
        verbose = TRUE,
        count_only = TRUE
    )
    save(nature_corpus_plus, file = fn)
} else {
    nature_corpus_plus <- readRDS(fn)
}
Show the code
#|

fn <- file.path(".", "data", paste0("tc_corpus_plus.rds"))
if (!file.exists(fn)) {
    tc_corpus_plus <- openalexR::oa_fetch(
        title_and_abstract.search = compact(params$s_1_transformative_change),
        concepts.id = paste(
            "C548081761",
            "C162324750",
            "C87717796",
            "C139719470",
            "C2778348673",
            "C175605778",
            "C47737302",
            "C17744445",
            "C199539241",
            "C144133560",
            "C138885662",
            "C94625758",
            "C10138342",
            "C50522688",
            "C162853370",
            "C77805123",
            "C36289849",
            "C39549134",
            "C187736073",
            "C188147891",
            "C66204764",
            "C199033989",
            "C74363100",
            "C2776867660",
            "C134560507",
            "C106159729",
            "C4249254",
            "C47768531",
            "C190248442",
            "C95124753",
            "C6303427",
            sep = "|"
        ),
        sustainable_development_goals.id = paste(
            "https://metadata.un.org/sdg/16",
            "https://metadata.un.org/sdg/10",
            "https://metadata.un.org/sdg/5",
            "https://metadata.un.org/sdg/4",
            "https://metadata.un.org/sdg/8",
            "https://metadata.un.org/sdg/1",
            "https://metadata.un.org/sdg/3",
            "https://metadata.un.org/sdg/2",
            "https://metadata.un.org/sdg/12",
            "https://metadata.un.org/sdg/9",
            "https://metadata.un.org/sdg/17",
            "https://metadata.un.org/sdg/11",
            "https://metadata.un.org/sdg/6",
            "https://metadata.un.org/sdg/7",
            sep = "|"
        ),
        options = list(
            select = "id, authorships"
        ),
        verbose = TRUE,
        count_only = TRUE
    )
    save(tc_corpus_plus, file = fn)
} else {
    tc_corpus_plus <- readRDS(fn)
}
Show the code
#|
fn <- file.path(".", "data", "key_papers.rds")
if (!file.exists(fn)) {
    key_papers <- lapply(
        params$key_papers,
        function(fn) {
            message("Processing '", fn, "' ...")
            sapply(
                fn,
                function(x) {
                    read.csv(x) |>
                        select(DOI)
                }
            ) |>
                unlist()
        }
    )
    names(key_papers) <- gsub("\\.csv", "", basename(params$key_papers))

    key_papers <- list(
        Ch_1 = unlist(key_papers[grepl("Ch 1 -", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_2 = unlist(key_papers[grepl("Ch 2 -", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3_Cl_1 = unlist(key_papers[grepl("Ch 3 - Cl1", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3_Cl_3 = unlist(key_papers[grepl("Ch 3 - Cl3", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3_Cl_4 = unlist(key_papers[grepl("Ch 3 - Cl4", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3_Cl_5 = unlist(key_papers[grepl("Ch 3 - Cl5", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3_Cl_6 = unlist(key_papers[grepl("Ch 3 - Cl6", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_3 = unlist(key_papers[grepl("Ch 3 - p", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_4_Cl_1 = unlist(key_papers[grepl("Ch 4 - Challenge 1", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_4_Cl_2 = unlist(key_papers[grepl("Ch 4 - Challenge 2", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_4_Cl_3 = unlist(key_papers[grepl("Ch 4 - Challenge 3", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_4_Cl_4 = unlist(key_papers[grepl("Ch 4 - Challenge 4", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_4_Cl_5 = unlist(key_papers[grepl("Ch 4 - Challenge 5", names(key_papers))], recursive = FALSE) |> as.vector(),
        Ch_5 = unlist(key_papers[grepl("Ch 5 -", names(key_papers))], recursive = FALSE) |> as.vector()
    )

    saveRDS(key_papers, file = fn)
} else {
    key_papers <- readRDS(fn)
}
Show the code
#|

fn_kw <- file.path(".", "data", "key_works.rds")
fn_kw_df <- file.path(".", "data", "key_works_df.rds")
if (!all(file.exists(fn_kw, fn_kw_df))) {
    key_works <- parallel::mclapply(
        key_papers,
        function(kp) {
            dois <- kp[kp != ""] |>
                unlist() |>
                tolower() |>
                unique()

            openalexR::oa_fetch(doi = dois, output = "list")
        },
        mc.cores = params$mc.cores,
        mc.preschedule = FALSE
    )

    found <- sapply(
        key_works,
        function(x) {
            length(x) > 0
        }
    )

    key_works <- key_works[found]

    print("The following key paper sets were excluded as they contained no papers in OpenAlex:\n")
    print(names(found)[!found])

    saveRDS(key_works, file = fn_kw)

    key_works_df <- lapply(
        key_works,
        oa2df,
        entity = "works"
    )

    saveRDS(key_works_df, fn_kw_df)
} else {
    key_works <- readRDS(file = fn_kw)
    key_works_df <- readRDS(fn_kw_df)
}
[1] "The following key paper sets were excluded as they contained no papers in OpenAlex:\n"
[1] "Ch_3_Cl_1"
Show the code
#|

fn <- file.path(".", "data", "key_works_hits.rds")
if (!file.exists(fn)) {
    kws <- key_works_df
    kws$all  <- key_works_df |>
        bind_rows()

    nms <- names(kws)
    key_works_hits <- pbapply::pblapply(
        nms,
        function(nm) {
            message("Getting key paper set for ", nm, " ...")
            dois <- kws[[nm]] |>
                select(doi) |>
                distinct() |>
                unlist() |>
                unique() |>
                tolower()

            s_t <- grep("s_1_", names(params), value = TRUE)
            kw_h <- parallel::mclapply(
                s_t,
                function(stn) {
                    message("  getting '", stn, "' ...")
                    if (grepl("_f_", stn)) {
                        search <- compact(params[[stn]]())
                    } else {
                        search <- compact(params[[stn]])
                    }
                    get_count(dois = dois, list(title_and_abstract.search = search), verbose = FALSE)
                },
                mc.cores = params$mc.cores,
                mc.preschedule = FALSE
            ) |>
                do.call(what = cbind) |>
                as.data.frame()

            names(kw_h) <- s_t

            # if (ncol(kw_h) == 1){
            #     kw_h <- t(kw_h)
            #     rownames(kw_h) <- dois
            # }

            kw_h <- rbind(
                kw_h,
                colSums(kw_h)
            )

            rownames(kw_h)[[nrow(kw_h)]] <- "Total"
            return(kw_h)  
        }
    )

    names(key_works_hits) <- nms

    for (i in nms) {
        # key_works_hits[[i]] <- cbind(
        #     key_works_hits[[i]],
        #     key_works_hits_tca_filtered[[i]]
        # )

        key_works_hits[[i]] <- cbind(
            key_works_hits[[i]],
            Total = rowSums(key_works_hits[[i]])
        ) |>
            mutate(Total = Total - 1) # |>
            # relocate(tca_corpus_SDG, .after = s_1_f_tca_corpus)
    }

    ###

    saveRDS(key_works_hits, file = fn)
} else {
    key_works_hits <- readRDS(file = fn)
}

Results

Number of Hits per Individual Corpus

Here we show the number of hits for the key papers in the different individual corpi. The columns represent the different search terms as defined in Section 2.1.

Show the code
dat <- cbind(
    search_term_hits
)

rownames(dat) <- dplyr::recode(
    rownames(dat),
    "transformative change" = "Transformative Change @sec-transform",
    "nature environment" = "Nature @sec-nature",
    "tca corpus" = "Assessment Corpus @sec-tca-corpus",
    "Chapter 1 01" = "Ch1 01 @sec-ch1-01",
    "Chapter 1 02" = "Ch1 02 @sec-ch1-02",
    "Chapter 1 03" = "Ch1 03 @sec-ch1-03",
    "Chapter 1 04" = "Ch1 04 @sec-ch1-04",
    "Chapter 1 05" = "Ch1 05 @sec-ch1-05",
    "Chapter 1 06" = "Ch1 06 @sec-ch1-06",
    "Chapter 2" = "Ch2  @sec-ch2",
    "Chapter 3 01" = "Ch3 01 @sec-ch3-01",
    "Chapter 3 02" = "Ch3 02 @sec-ch3-02",
    "Chapter 3 03" = "Ch3 03 @sec-ch3-03",
    "Chapter 3 04" = "Ch3 04 @sec-ch3-04",
    "Chapter 3 05" = "Ch3 05 @sec-ch3-05",
    "Chapter 3 06" = "Ch3 06 @sec-ch3-06",
    "Chapter 4 01" = "Ch4 01 @sec-ch4-01",
    "Chapter 4 02" = "Ch4 02 @sec-ch4-02",
    "Chapter 5 vision" = "Ch5 Vision @sec-ch5_vision",
    "Chapter 5 vision case" = "Ch5 Vision Case @sec-ch5_vision_case",
    "case" = "Ch5 Case @sec-case"
)

dat |>
    knitr::kable(
        caption = "Number of hits",
    )
Number of hits
count db_response_time_ms
oa 248,874,519 175
Transformative Change Section 2.1.1 18,280,233 2861
Nature Section 2.1.2 24,090,077 2602
Assessment Corpus Section 2.1.3 4,532,896 2741
Ch1 01 Section 2.1.4.1 620,749 235
Ch1 02 Section 2.1.4.2 3,163,296 279
Ch1 03 Section 2.1.4.3 10,626,721 520
Ch1 04 Section 2.1.4.4 2,453,272 436
Ch1 05 Section 2.1.4.5 25,456,976 939
Ch1 06 Section 2.1.4.6 6,372,486 625
Ch2 Section 2.1.5 104,988,291 6054
Ch3 01 Section 2.1.6.1 10,492,523 846
Ch3 02 Section 2.1.6.2 33,044,281 1432
Ch3 03 Section 2.1.6.3 28,321,306 809
Ch3 04 Section 2.1.6.4 10,574,448 684
Ch3 05 Section 2.1.6.5 12,943,751 724
Ch3 06 Section 2.1.6.6 19,946,078 1047
Ch4 01 Section 2.1.7.1 855,604 971
Ch4 02 Section 2.1.7.2 8 857
Ch5 Case Section 2.1.8.2 34,210,926 1689
Chapter 2 vision case 26,820,561 4302
Show the code
rm(dat)

Key papers in different Individual Corpi

Show the code
#|

tbl <- lapply(
    names(key_works_hits),
    function(n) {
        kwh <- key_works_hits[[n]]
        if (nrow(kwh) > 0) {
            total <- grepl("Total", rownames(kwh))
            rownames(kwh)[!total] <- paste0(n, " - <a href='https://doi.org/", rownames(kwh)[!total], "' target='_blank'>Click here</a>")
            rownames(kwh)[total] <- paste0("**", n, " - Total**")
            kwh |>
                arrange(Total) |>
                apply(
                    c(1, 2),
                    function(x) {
                        ifelse(x == 0, "<font color='red'>0</font>", paste0("<font color='green'>", x, "</font>"))
                    }
                ) |>
                as.data.frame()
        } else {
            return(NULL)
        }
    }
)
tbl <- tbl[sapply(tbl, class) != "NULL"]
tbl <- do.call(what = rbind, tbl)


detail <- rbind(
    "**overall**" = c(
        paste0(
            "**",
            search_term_hits |>
                select(count) |>
                unlist() |>
                as.vector(),
            "**"
        ),
        ""
    ),
    tbl
)

detail <- detail |>
    dplyr::rename(
        "Transformative Change @sec-transform" = s_1_transformative_change,
        "Nature @sec-nature" = s_1_nature_environment,
        "Assessment Corpus @sec-tca-corpus" = s_1_f_tca_corpus,
        "Ch1 01 @sec-ch1-01" = s_1_ch1_01,
        "Ch1 02 @sec-ch1-02" = s_1_ch1_02,
        "Ch1 03 @sec-ch1-03" = s_1_ch1_03,
        "Ch1 04 @sec-ch1-04" = s_1_ch1_04,
        "Ch1 05 @sec-ch1-05" = s_1_ch1_05,
        "Ch1 06 @sec-ch1-06" = s_1_ch1_06,
        "Ch2  @sec-ch2" = s_1_ch2,
        "Ch3 01 @sec-ch3-01" = s_1_ch3_01,
        "Ch3 02 @sec-ch3-02" = s_1_ch3_02,
        "Ch3 03 @sec-ch3-03" = s_1_ch3_03,
        "Ch3 04 @sec-ch3-04" = s_1_ch3_04,
        "Ch3 05 @sec-ch3-05" = s_1_ch3_05,
        "Ch3 06 @sec-ch3-06" = s_1_ch3_06,
        "Ch4 01 @sec-ch4-01" = s_1_ch4_01,
        "Ch4 02 @sec-ch4-02" = s_1_ch4_02,
        # "Ch5 Vision @sec-ch5_vision" = s_1_ch5_vision,
        "Ch5 Case @sec-case" = s_1_case,
        # "Ch5 Vision Case @sec-ch5_vision_case" = s_1_f_ch5_vision_case
    )

Key Papers in Individual Corpi

Summary

Each column is a different search term, and each row consists of the key papers of a specific chapter and the author who provided the key papers. The number is the number of key papers occurring in the Individual Corpus.

Show the code
in_summary <- grepl("Total|overall", rownames(detail))
knitr::kable(
    detail[in_summary, ]
)
s_1_oa Transformative Change Section 2.1.1 Nature Section 2.1.2 Assessment Corpus Section 2.1.3 Ch1 01 Section 2.1.4.1 Ch1 02 Section 2.1.4.2 Ch1 03 Section 2.1.4.3 Ch1 04 Section 2.1.4.4 Ch1 05 Section 2.1.4.5 Ch1 06 Section 2.1.4.6 Ch2 Section 2.1.5 Ch3 01 Section 2.1.6.1 Ch3 02 Section 2.1.6.2 Ch3 03 Section 2.1.6.3 Ch3 04 Section 2.1.6.4 Ch3 05 Section 2.1.6.5 Ch3 06 Section 2.1.6.6 Ch4 01 Section 2.1.7.1 Ch4 02 Section 2.1.7.2 Ch5 Case Section 2.1.8.2 s_1_f_ch2_vision_case Total
overall 248,874,519 18,280,233 24,090,077 4,532,896 620,749 3,163,296 10,626,721 2,453,272 25,456,976 6,372,486 104,988,291 10,492,523 33,044,281 28,321,306 10,574,448 12,943,751 19,946,078 855,604 8 34,210,926 26,820,561
Ch_1 - Total 42 38 39 36 10 18 15 21 19 18 40 21 23 24 12 23 26 14 0 25 25 488
Ch_2 - Total 22 17 22 17 3 12 8 7 9 12 21 11 13 14 10 13 19 10 0 11 11 261
Ch_3_Cl_3 - Total 4 4 4 4 3 1 3 2 4 4 4 4 4 4 4 4 4 3 0 4 4 71
Ch_3_Cl_4 - Total 5 5 4 4 2 3 2 3 2 3 4 4 5 5 4 3 3 2 0 3 3 68
Ch_3_Cl_5 - Total 3 2 3 2 0 1 0 1 1 0 2 2 0 1 0 1 1 0 0 1 1 21
Ch_3_Cl_6 - Total 6 6 5 5 1 1 1 3 1 3 5 2 4 5 1 1 5 2 0 3 3 62
Ch_3 - Total 4 4 4 4 2 1 2 2 3 3 4 3 4 3 1 2 3 2 0 4 4 58
Ch_4_Cl_1 - Total 7 2 7 2 0 2 3 3 3 4 7 3 3 3 2 6 6 2 0 3 3 70
Ch_4_Cl_2 - Total 4 2 3 2 1 2 1 1 1 1 3 1 2 2 1 2 3 1 0 2 2 36
Ch_4_Cl_3 - Total 5 5 5 5 0 1 1 0 1 2 4 1 3 3 1 3 4 1 0 4 4 52
Ch_4_Cl_4 - Total 4 2 4 2 1 1 3 1 1 2 4 2 1 2 2 2 4 1 0 3 3 44
Ch_4_Cl_5 - Total 5 3 4 3 1 1 2 1 2 1 5 2 3 3 2 2 4 1 0 4 4 52
Ch_5 - Total 35 32 32 29 10 15 17 16 18 17 35 22 28 29 21 24 24 8 0 19 19 449
all - Total 134 111 125 105 32 54 54 57 62 66 127 71 84 89 55 80 99 45 0 79 79 1607

Detail

Show the code
knitr::kable(
    detail
)
s_1_oa Transformative Change Section 2.1.1 Nature Section 2.1.2 Assessment Corpus Section 2.1.3 Ch1 01 Section 2.1.4.1 Ch1 02 Section 2.1.4.2 Ch1 03 Section 2.1.4.3 Ch1 04 Section 2.1.4.4 Ch1 05 Section 2.1.4.5 Ch1 06 Section 2.1.4.6 Ch2 Section 2.1.5 Ch3 01 Section 2.1.6.1 Ch3 02 Section 2.1.6.2 Ch3 03 Section 2.1.6.3 Ch3 04 Section 2.1.6.4 Ch3 05 Section 2.1.6.5 Ch3 06 Section 2.1.6.6 Ch4 01 Section 2.1.7.1 Ch4 02 Section 2.1.7.2 Ch5 Case Section 2.1.8.2 s_1_f_ch2_vision_case Total
overall 248,874,519 18,280,233 24,090,077 4,532,896 620,749 3,163,296 10,626,721 2,453,272 25,456,976 6,372,486 104,988,291 10,492,523 33,044,281 28,321,306 10,574,448 12,943,751 19,946,078 855,604 8 34,210,926 26,820,561
Ch_1 - Click here 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ch_1 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2
Ch_1 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 3
Ch_1 - Click here 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 5
Ch_1 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 6
Ch_1 - Click here 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 6
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 6
Ch_1 - Click here 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 6
Ch_1 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 7
Ch_1 - Click here 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 7
Ch_1 - Click here 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 8
Ch_1 - Click here 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 1 9
Ch_1 - Click here 1 1 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 9
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 10
Ch_1 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 10
Ch_1 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 0 0 10
Ch_1 - Click here 1 1 1 1 0 0 1 0 0 0 1 1 0 1 0 1 0 0 0 1 1 10
Ch_1 - Click here 1 1 1 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 0 1 1 10
Ch_1 - Click here 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 10
Ch_1 - Click here 1 1 1 1 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 0 0 10
Ch_1 - Click here 1 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 11
Ch_1 - Click here 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 11
Ch_1 - Click here 1 1 1 1 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 1 1 12
Ch_1 - Click here 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 13
Ch_1 - Click here 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 14
Ch_1 - Click here 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 15
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 1 16
Ch_1 - Click here 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
Ch_1 - Click here 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
Ch_1 - Click here 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
Ch_1 - Click here 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 17
Ch_1 - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 18
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_1 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_1 - Total 42 38 39 36 10 18 15 21 19 18 40 21 23 24 12 23 26 14 0 25 25 488
Ch_2 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3
Ch_2 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3
Ch_2 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
Ch_2 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 5
Ch_2 - Click here 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 5
Ch_2 - Click here 1 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 6
Ch_2 - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 8
Ch_2 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 9
Ch_2 - Click here 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 1 9
Ch_2 - Click here 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 1 1 0 1 1 9
Ch_2 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 10
Ch_2 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 1 1 11
Ch_2 - Click here 1 1 1 1 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 1 1 11
Ch_2 - Click here 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 12
Ch_2 - Click here 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 0 0 13
Ch_2 - Click here 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 16
Ch_2 - Click here 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 16
Ch_2 - Click here 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 17
Ch_2 - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_2 - Click here 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_2 - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_2 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_2 - Total 22 17 22 17 3 12 8 7 9 12 21 11 13 14 10 13 19 10 0 11 11 261
Ch_3_Cl_3 - Click here 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 16
Ch_3_Cl_3 - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 17
Ch_3_Cl_3 - Click here 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 17
Ch_3_Cl_3 - Click here 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_3_Cl_3 - Total 4 4 4 4 3 1 3 2 4 4 4 4 4 4 4 4 4 3 0 4 4 71
Ch_3_Cl_4 - Click here 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 6
Ch_3_Cl_4 - Click here 1 1 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 9
Ch_3_Cl_4 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 1 1 11
Ch_3_Cl_4 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_3_Cl_4 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_3_Cl_4 - Total 5 5 4 4 2 3 2 3 2 3 4 4 5 5 4 3 3 2 0 3 3 68
Ch_3_Cl_5 - Click here 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ch_3_Cl_5 - Click here 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 5
Ch_3_Cl_5 - Click here 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 13
Ch_3_Cl_5 - Total 3 2 3 2 0 1 0 1 1 0 2 2 0 1 0 1 1 0 0 1 1 21
Ch_3_Cl_6 - Click here 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 5
Ch_3_Cl_6 - Click here 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 6
Ch_3_Cl_6 - Click here 1 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 7
Ch_3_Cl_6 - Click here 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 1 1 9
Ch_3_Cl_6 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 1 1 11
Ch_3_Cl_6 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_3_Cl_6 - Total 6 6 5 5 1 1 1 3 1 3 5 2 4 5 1 1 5 2 0 3 3 62
Ch_3 - Click here 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 1 9
Ch_3 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 10
Ch_3 - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_3 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 18
Ch_3 - Total 4 4 4 4 2 1 2 2 3 3 4 3 4 3 1 2 3 2 0 4 4 58
Ch_4_Cl_1 - Click here 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 4
Ch_4_Cl_1 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 4
Ch_4_Cl_1 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 5
Ch_4_Cl_1 - Click here 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 6
Ch_4_Cl_1 - Click here 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 0 0 9
Ch_4_Cl_1 - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_4_Cl_1 - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_4_Cl_1 - Total 7 2 7 2 0 2 3 3 3 4 7 3 3 3 2 6 6 2 0 3 3 70
Ch_4_Cl_2 - Click here 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ch_4_Cl_2 - Click here 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 6
Ch_4_Cl_2 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 8
Ch_4_Cl_2 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_4_Cl_2 - Total 4 2 3 2 1 2 1 1 1 1 3 1 2 2 1 2 3 1 0 2 2 36
Ch_4_Cl_3 - Click here 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 5
Ch_4_Cl_3 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 7
Ch_4_Cl_3 - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 10
Ch_4_Cl_3 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 1 1 10
Ch_4_Cl_3 - Click here 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 16
Ch_4_Cl_3 - Total 5 5 5 5 0 1 1 0 1 2 4 1 3 3 1 3 4 1 0 4 4 52
Ch_4_Cl_4 - Click here 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 5
Ch_4_Cl_4 - Click here 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 9
Ch_4_Cl_4 - Click here 1 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 10
Ch_4_Cl_4 - Click here 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 17
Ch_4_Cl_4 - Total 4 2 4 2 1 1 3 1 1 2 4 2 1 2 2 2 4 1 0 3 3 44
Ch_4_Cl_5 - Click here 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 5
Ch_4_Cl_5 - Click here 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 6
Ch_4_Cl_5 - Click here 1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 10
Ch_4_Cl_5 - Click here 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 10
Ch_4_Cl_5 - Click here 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 17
Ch_4_Cl_5 - Total 5 3 4 3 1 1 2 1 2 1 5 2 3 3 2 2 4 1 0 4 4 52
Ch_5 - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2
Ch_5 - Click here 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 4
Ch_5 - Click here 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 4
Ch_5 - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 7
Ch_5 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 7
Ch_5 - Click here 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 9
Ch_5 - Click here 1 1 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 9
Ch_5 - Click here 1 1 1 1 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 9
Ch_5 - Click here 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 9
Ch_5 - Click here 1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 10
Ch_5 - Click here 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 10
Ch_5 - Click here 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 10
Ch_5 - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 10
Ch_5 - Click here 1 1 1 1 0 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 0 10
Ch_5 - Click here 1 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 0 10
Ch_5 - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 10
Ch_5 - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 1 1 11
Ch_5 - Click here 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 11
Ch_5 - Click here 1 1 1 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 1 11
Ch_5 - Click here 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 11
Ch_5 - Click here 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 11
Ch_5 - Click here 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 12
Ch_5 - Click here 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1 1 0 0 0 12
Ch_5 - Click here 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 14
Ch_5 - Click here 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 15
Ch_5 - Click here 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 15
Ch_5 - Click here 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 0 1 1 15
Ch_5 - Click here 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 16
Ch_5 - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_5 - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
Ch_5 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_5 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_5 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_5 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_5 - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
Ch_5 - Total 35 32 32 29 10 15 17 16 18 17 35 22 28 29 21 24 24 8 0 19 19 449
all - Click here 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
all - Click here 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
all - Click here 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 3
all - Click here 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 4
all - Click here 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 4
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 4
all - Click here 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 4
all - Click here 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 4
all - Click here 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 5
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 5
all - Click here 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 5
all - Click here 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 5
all - Click here 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 5
all - Click here 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 5
all - Click here 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 5
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 6
all - Click here 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 6
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 6
all - Click here 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 6
all - Click here 1 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 6
all - Click here 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 6
all - Click here 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 6
all - Click here 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 6
all - Click here 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 6
all - Click here 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 6
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 7
all - Click here 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 7
all - Click here 1 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 7
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 7
all - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 7
all - Click here 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 7
all - Click here 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 8
all - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 8
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 8
all - Click here 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 1 9
all - Click here 1 1 0 0 0 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 9
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 9
all - Click here 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 1 9
all - Click here 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 1 1 0 1 1 9
all - Click here 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 1 1 9
all - Click here 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 0 0 9
all - Click here 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 9
all - Click here 1 1 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 9
all - Click here 1 1 1 1 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 9
all - Click here 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 9
all - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 10
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 10
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 0 0 10
all - Click here 1 1 1 1 0 0 1 0 0 0 1 1 0 1 0 1 0 0 0 1 1 10
all - Click here 1 1 1 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 0 1 1 10
all - Click here 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 10
all - Click here 1 1 1 1 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 0 0 10
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 10
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 1 1 10
all - Click here 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 1 1 10
all - Click here 1 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 10
all - Click here 1 1 1 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 10
all - Click here 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 10
all - Click here 1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 10
all - Click here 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 10
all - Click here 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 10
all - Click here 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 10
all - Click here 1 1 1 1 0 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 0 10
all - Click here 1 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 0 10
all - Click here 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 10
all - Click here 1 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 11
all - Click here 1 1 1 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 11
all - Click here 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 1 1 11
all - Click here 1 1 1 1 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 1 1 11
all - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 1 1 11
all - Click here 1 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 1 1 11
all - Click here 1 1 1 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 1 11
all - Click here 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 11
all - Click here 1 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 11
all - Click here 1 1 1 1 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 1 1 12
all - Click here 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 12
all - Click here 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 12
all - Click here 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 1 1 1 0 0 0 12
all - Click here 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 13
all - Click here 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 0 0 13
all - Click here 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 13
all - Click here 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1 14
all - Click here 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 15
all - Click here 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 15
all - Click here 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 15
all - Click here 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 0 1 1 15
all - Click here 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 1 16
all - Click here 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
all - Click here 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
all - Click here 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 16
all - Click here 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 16
all - Click here 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 16
all - Click here 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 16
all - Click here 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 16
all - Click here 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 16
all - Click here 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 17
all - Click here 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 17
all - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 17
all - Click here 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 17
all - Click here 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 17
all - Click here 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 17
all - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 18
all - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 18
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Click here 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 19
all - Total 134 111 125 105 32 54 54 57 62 66 127 71 84 89 55 80 99 45 0 79 79 1607

TCA Corpus properties

TODO Countries in TCA Corpus

Show the code
map <- countries_tca_corpus |>
    mutate(
        log_count = log(count)
    ) |>
    map_country_codes(
        values = "log_count",
        map_type = "countries"
    )
Warning: Some values were not matched unambiguously: XK
Warning in map_country_codes(mutate(countries_tca_corpus, log_count = log(count)), : The following countries are not in the world dataset: 
AIA, BLM, BMU, CXR, GGY, GIB, IOT, MCO, MHL, MDV, NFK, NRU, SMR, TKL, TUV, UMI, VAT, VGB, WLF, NA
and will therefore not be plotted!
Show the code
map

Topics in corpus

Show the code
#|

cs <- cumsum(prim_topics_tca_corpus$count)
cs |>
    plot(
        type = "l",
        xlab = "Topic",
        ylab = "Cumulative Count",
        main = "Cumulative Topics in TCA Corpus"
    )

abline(
    h = 0.95 * cs[length(cs)],
    v = min(which(cs > 0.95 * cs[length(cs)])),
    col = "red"
)

text(
    x = 0.5 * length(cs),
    y = 0.95 * cs[length(cs)],
    pos = 3,
    labels = "95% of the corpus",
    col = "red"
)

Show the code
#|

prim_topics_tca_corpus |>
    relocate(count, .after = "topic_id") |>
    DT::datatable(
        extensions = c(
            "Buttons",
            "FixedColumns",
            "Scroller"
        ),
        options = list(
            dom = "Bfrtip",
            buttons = list(
                list(
                    extend = "csv",
                    filename = fn
                ),
                list(
                    extend = "excel",
                    filename = fn
                ),
                list(
                    extend = "pdf",
                    filename = fn,
                    orientation = "landscape",
                    customize = DT::JS(
                        "function(doc) {",
                        "  doc.defaultStyle.fontSize = 5;", # Change the font size
                        "}"
                    )
                ),
                "print"
            ),
            scroller = TRUE,
            scrollY = JS("window.innerHeight * 0.7 + 'px'"),
            scrollX = TRUE,
            fixedColumns = list(leftColumns = 4)
        ),
        escape = FALSE
    )
Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

SubFields in Corpus

Show the code
#|

prim_topics_tca_corpus |>
    mutate(
        topic_id = NULL,
        topic_name = NULL,
        keywords = NULL,
        summary = NULL,
        wikipedia_url = NULL
    ) |>
    group_by(
        subfield_id,
        subfield_name,
        field_id,
        field_name,
        domain_id,
        domain_name
    ) |>
    summarise(
        count = sum(count)
    ) |>
    arrange(desc(count)) |>
    relocate(count, .after = "subfield_id") |>
    DT::datatable(
        extensions = c(
            "Buttons",
            "FixedColumns",
            "Scroller"
        ),
        options = list(
            dom = "Bfrtip",
            buttons = list(
                list(
                    extend = "csv",
                    filename = fn
                ),
                list(
                    extend = "excel",
                    filename = fn
                ),
                list(
                    extend = "pdf",
                    filename = fn,
                    orientation = "landscape",
                    customize = DT::JS(
                        "function(doc) {",
                        "  doc.defaultStyle.fontSize = 5;", # Change the font size
                        "}"
                    )
                ),
                "print"
            ),
            scroller = TRUE,
            scrollY = JS("window.innerHeight * 0.7 + 'px'"),
            scrollX = TRUE,
            fixedColumns = list(leftColumns = 4)
        ),
        escape = FALSE
    )
  • TODO: Histogram / table with topics identified to identify non-relevant papers

Reuse

Citation

BibTeX citation:
@report{m krug2024,
  author = {M Krug, Rainer},
  title = {Data {Management} {Report} {Transformative} {Change}
    {Assessment} {Corpus} - {SOD}},
  date = {2024-02-20},
  doi = {10.5281/zenodo.10251349},
  langid = {en},
  abstract = {The literature search for the assessment corpus was
    conducted using search terms provided by the experts and refined in
    co-operation with the Knowldge and Data task force. The search was
    conducted using {[}OpenAlex{]}(https://openalex.org), scripted from
    {[}R{]}(https://cran.r-project.org) to use the
    {[}API{]}(https://docs.openalex.org). Search terms for the following
    searches were defined: **Transformative Change**, **Nature /
    Environment** and **additional search terms for individual chapters
    and sub-chapters** To assess the quality of the corpus, sets of
    key-papers were selected by the experts to verify if these are in
    the corpus. These key-papers were selected per chapter / sub-chapter
    to ensure that the corpus is representative of each chapter.}
}
For attribution, please cite this work as:
M Krug, Rainer. 2024. “Data Management Report Transformative Change Assessment Corpus - SOD.” Report Transformative Change Assessment Corpus. https://doi.org/10.5281/zenodo.10251349.